If you spend enough time reading educational studies, you're bound to come across the inclusion of algebraic formulas. Even if you don't understand them, you have to admit they are impressive. But as Carl Bialik, who is known as the Numbers Guy, wrote: "Don't Let Math Pull the Wool Over Your Eyes" (The Wall Street Journal, Jan. 4).
What Bialik meant is that "numbers can warp rather than enhance logical thinking." It's not just lay readers who are affected but scientific researchers as well. I was thinking in particular about the value-added metric in this regard. The claim made by its supporters is that the model takes into account factors beyond the control of teachers. So I turned to Chance, which bills itself as A Magazine for People Interested in the Analysis of Data. The article I chose was intriguingly titled "Value-Added Models to Evaluate Teachers: A Cry For Help." I got through the first page without any trouble. The second page was a different story. My keyboard simply does not have the characters needed to reproduce the formulas here.
Perhaps in anticipation of my ignorance, the writer "explained" the formula for the first year as follows: "Student's score (1) = district average (1) + teacher effect (1) + error (1)." Lest readers like me still failed to understand, the writer then wrote: "There are similar equations for the second, third, fourth, and fifth years, and it is instructive to look at the second year's equation, which looks like the first except it contains a term for the teacher's effect from the previous year." In comparison, the instructional manual provided by the IRS for Form 1040 is child's play.
The point is that we are too accepting of research that relies heavily on esoteric formulas. I want evidence to support conclusions about educational issues. But the evidence has to be understandable. Just as legal contracts now are increasingly written with consumers in mind, I hope that educational studies will do the same in the future. Taxpayers are entitled to know if students are being well taught, but they can't make that judgment when they are given incomprehensible data.